Machine-learning study using improved correlation configuration and application to quantum Monte Carlo simulation

Yusuke Tomita, Kenta Shiina, Yutaka Okabe, Hwee Kuan Lee

研究成果: Article

抜粋

We use the Fortuin-Kasteleyn representation-based improved estimator of the correlation configuration as an alternative to the ordinary correlation configuration in the machine-learning study of the phase classification of spin models. The phases of classical spin models are classified using the improved estimators, and the method is also applied to the quantum Monte Carlo simulation using the loop algorithm. We analyze the Berezinskii-Kosterlitz-Thouless (BKT) transition of the spin-1/2 quantum XY model on the square lattice. We classify the BKT phase and the paramagnetic phase of the quantum XY model using the machine-learning approach. We show that the classification of the quantum XY model can be performed by using the training data of the classical XY model.

元の言語English
記事番号021302
ジャーナルPhysical Review E
102
発行部数2
DOI
出版物ステータスPublished - 2020 8

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

フィンガープリント Machine-learning study using improved correlation configuration and application to quantum Monte Carlo simulation' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用